2023 Intelligent Methods, Systems, and Applications (IMSA)最新文献_第4页

Maximum Power Point Tracking Using Model Predictive Control For Photovoltaic Systems* 基于模型预测控制的光伏系统最大功率点跟踪*

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217595

Youmna A. Abu-Krisha, A. El-Badawy, W. Omran

{"title":"Maximum Power Point Tracking Using Model Predictive Control For Photovoltaic Systems*","authors":"Youmna A. Abu-Krisha, A. El-Badawy, W. Omran","doi":"10.1109/IMSA58542.2023.10217595","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217595","url":null,"abstract":"The employment of photovoltaic (PV) power generation has garnered significant attention in industrial sector as of late. Renewable and alternative energy sources possess superior attributes, including being clean and quiet, as well as having less maintenance issues than conventional energy sources. The expansion of photovoltaic systems has been significantly influenced by their efficiency. Numerous control strategies have been explored to optimize the power output of photovoltaic systems in diverse environmental conditions. The present study investigates the various techniques employed for Maximum Power Point Tracking (MPPT), with a particular emphasis on the model predictive control (MPC) approach. Various Maximum Power Point Tracking techniques are simulated on a Photovoltaic module that is connected to a Direct Current-Direct Current (DC-DC) boost converter. The performance of the subject is demonstrated through simulation results while being subjected to varying solar irradiance and temperature. The comparative analysis of Perturb and Observe with Model Predictive Control technique (P&O with MPC) is conducted and compared to alternative techniques to determine the most efficient approach.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129058276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Material Classification in Laser Cutting using Deep Learning 基于深度学习的激光切割材料分类

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217625

Mohamed Abdallah Salem, A. Elshenawy, H. Ashour

{"title":"Material Classification in Laser Cutting using Deep Learning","authors":"Mohamed Abdallah Salem, A. Elshenawy, H. Ashour","doi":"10.1109/IMSA58542.2023.10217625","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217625","url":null,"abstract":"Industrial laser cutting offers numerous advantages, including high speed, precision, and the ability to process a wide range of materials. However, the versatility of this technology presents a significant challenge when determining the appropriate power and speed for cutting materials. As the laser cutting machine operator selects the cutting parameters manually, confusion can quickly arise when cutting visually similar materials. This confusion can directly affect the cutting quality and result in errors and wasted materials. This study presents an automated material classification approach based on deep learning that utilizes convolutional neural networks (CNN) and laser speckle sensing. A comprehensive dataset of speckle pattern images for the most commonly used laser-cutting materials is proposed in this paper. The dataset is created by capturing the scattered speckle patterns of the material being cut using a USB camera and includes 6000 speckle pattern images. A deep-learning model based on the pre-trained VGG16 model is then trained on the proposed dataset. The performance of the model is experimentally evaluated on a test set of 200 images with varying orientations and lighting conditions for each sample. The F1-score, recall, and precision metrics validate the robustness of the model in distinguishing between the used materials. The results demonstrate the ability of the model to classify the materials, achieving a notably high classification accuracy of 98.1%. This approach provides an automated and cost-effective solution for organizing laser-cutting materials, enabling operators to select the appropriate power and speed for the cutting process based on the type of material being cut.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114185314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

News Presenter skills evaluation using multi-modality and machine learning 使用多模态和机器学习的新闻主持人技能评估

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217597

Ahmed Mohamed Emam, Mohamed AbdelAzim Elgarh, Amany S. Fahmy, Amira Abdel Moniem, Ayman Atia

{"title":"News Presenter skills evaluation using multi-modality and machine learning","authors":"Ahmed Mohamed Emam, Mohamed AbdelAzim Elgarh, Amany S. Fahmy, Amira Abdel Moniem, Ayman Atia","doi":"10.1109/IMSA58542.2023.10217597","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217597","url":null,"abstract":"Assessing television presenters is a challenging yet essential task, as it requires considering numerous characteristics for their evaluation. A multi-modal approach is employed, utilizing various data sources such as eye gaze, gestures, and facial expressions. Automation of this process is crucial due to the exhaustive nature of presenter evaluation, where assessors need to evaluate the presenter based on all the aforementioned features. This paper proposes a system that assesses the presenter based on four key features, namely posture, eye contact, facial expression, and voice. Each feature is assigned a weight, and the presenter receives a grade based on their performance on each feature. The present study focused on facial emotion, eye tracking, and physical posture. The presenter’s elbow, shoulder, and nose joints were extracted, and they served as inputs for classifiers that were divided into three categories: machine learning algorithms, template-based algorithms, and deep learning algorithms to classify the presenter’s posture. For the eye gaze distance algorithms such as Euclidean distance and Manhattan distance were employed to analyze eye gaze, while facial expression analysis was conducted using the DeepFace library. The system proposed in this research paper achieved an accuracy of 92% utilizing SVM in the machine learning algorithms, 75% using dollarpy in the distance algorithm, besides 79% utilizing BiLSTM for the deep learning model. The data set used in this study was collected from faculty of Mass communication, MSA University.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115351477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Abstractive Arabic Text Summarization Based on MT5 and AraBart Transformers 基于MT5和AraBart transformer的阿拉伯语抽象文本摘要

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217539

Asmaa H. Elsaid, A. Mohammed, L. Fattouh, M. Sakre

{"title":"Abstractive Arabic Text Summarization Based on MT5 and AraBart Transformers","authors":"Asmaa H. Elsaid, A. Mohammed, L. Fattouh, M. Sakre","doi":"10.1109/IMSA58542.2023.10217539","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217539","url":null,"abstract":"Text summarization is essential in natural language processing because of the rapid growth of data. Therefore, the user needs to summarize this data into meaningful text quickly. There are two standard methods of text summarization: extractive and abstractive. There are many efforts to summarize Latin texts. However, summarizing Arabic texts is challenging for many reasons, including the language’s complexity, structure, and morphology. Also, there is a need for benchmark data sources and a gold-standard Arabic evaluation metrics summary. Thus, the contribution of this research is multi-fold: First, it introduces a new Arabic benchmark dataset, called the HASD, which includes 43k articles with their extractive and abstractive summaries. Second, it presents a new Arabic benchmark dataset called the AASD, which includes 150k articles with their abstractive summaries. Third, this work modifies the well-known extractive EASC benchmarks by adding to each text its abstractive summarization. Fourth, this paper proposes a new measure called the Arabic-rouge measure for the abstractive summary depending on structure and similarity between words. Finally, an investigation of the impact of using abstractive Arabic text summarization on different transformer models with different data sets. The model is tested using the proposed HASD, AASD, modified EASC benchmarks and evaluated using Rouge, Bleu, and Arabic Rouge. The experimental results show satisfactory results compared to state-of-the-art methods.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126993332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Malaria Infection Using Convolutional Neural Networks 基于卷积神经网络的疟疾感染检测

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217455

Farah Sherif, Ammar F. Mohammed

{"title":"Detection of Malaria Infection Using Convolutional Neural Networks","authors":"Farah Sherif, Ammar F. Mohammed","doi":"10.1109/IMSA58542.2023.10217455","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217455","url":null,"abstract":"A parasite that causes the potentially fatal disease malaria is spread to humans by female Anopheles mosquitoes carrying this parasite. Despite global efforts to eradicate the disease, malaria is still a major public health problem, particularly in underdeveloped nations. The timely and accurate detection of malaria infection is necessary for effective treatment and for reducing the morbidity and mortality associated with the disease. However, identifying malaria-infected cells can be challenging, especially in areas with limited access to trained healthcare professionals. Recent advances in deep learning and computer vision have shown promising results in identifying several medical images. This paper proposes a new CNN model to identify whether cells are parasitized or uninfected with malaria to help health personnel save the lives of infected people. In various ways, the proposed CNN architecture is compared to other pre-trained models, VGG-19, ResNet50, DenseNet121, and Inception V3. The malaria cell dataset of 27,558 images is used for model evaluations. The findings show that the proposed model performs the best, with an accuracy score of 97%.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128047023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End of Session (2) 会议结束(2)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217484

引用次数: 0

Session (17) 会话(17)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217687

K. Ravikumar, R. Wampler, M. Ho, J., Christensen, L. S., Phoa, Zhiguo Qian, Mario Pacheco, Zhiyong Wang, Rajen, Dias, V. Talanov, David, P. Vallett, Daniel A. Bader, Vladimir, V., Talanov, J. Gaudestad, N. Gagliolo, Antonio Orozco, Lihong Cao, Manasa, Venkata, Meng Yeow, Tay, Wen, Qiu, Alton, P. Taday, M., Igarashi, F. Lopour, Zadrazil, A. Delobbe, O. Salord

引用次数: 0

Multiple Attack Classification in IoT Networks 物联网网络中的多重攻击分类

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217377

Anfal Dawah, M. Al-Rousan, Ahmed S. Shatnawi, Basheer Al-Duwairi

引用次数: 0

End of session (18) 会议结束(18)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217547

引用次数: 0

Deep Learning Approach for Liver Tumor Diagnosis 肝脏肿瘤诊断的深度学习方法

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217588

Manal Makram, M. Elhemeily, Ammar Mohammed

引用次数: 1